Overview

Brought to you by YData

Dataset statistics

Number of variables41
Number of observations4337
Missing cells2623
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 MiB
Average record size in memory1.3 KiB

Variable types

Numeric27
Text7
Categorical7

Alerts

Month has 48 (1.1%) missing values Missing
Day has 380 (8.8%) missing values Missing
Region has 367 (8.5%) missing values Missing
District has 736 (17.0%) missing values Missing
City has 988 (22.8%) missing values Missing
UN is highly skewed (γ1 = 25.73259301) Skewed
Incident ID is uniformly distributed Uniform
Incident ID has unique values Unique
UN has 3423 (78.9%) zeros Zeros
INGO has 2346 (54.1%) zeros Zeros
ICRC has 4223 (97.4%) zeros Zeros
NRCS and IFRC has 4075 (94.0%) zeros Zeros
NNGO has 3241 (74.7%) zeros Zeros
Other has 4270 (98.5%) zeros Zeros
Nationals killed has 2590 (59.7%) zeros Zeros
Nationals wounded has 2619 (60.4%) zeros Zeros
Nationals kidnapped has 3556 (82.0%) zeros Zeros
Total nationals has 378 (8.7%) zeros Zeros
Internationals killed has 4161 (95.9%) zeros Zeros
Internationals wounded has 4148 (95.6%) zeros Zeros
Internationals kidnapped has 4136 (95.4%) zeros Zeros
Total internationals has 3799 (87.6%) zeros Zeros
Total killed has 2452 (56.5%) zeros Zeros
Total wounded has 2464 (56.8%) zeros Zeros
Total kidnapped has 3416 (78.8%) zeros Zeros
Gender Male has 1669 (38.5%) zeros Zeros
Gender Female has 3864 (89.1%) zeros Zeros
Gender Unknown has 2834 (65.3%) zeros Zeros

Reproduction

Analysis started2025-04-08 01:31:34.860289
Analysis finished2025-04-08 01:32:57.198391
Duration1 minute and 22.34 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

Incident ID
Real number (ℝ)

Uniform  Unique 

Distinct4337
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2255.4012
Minimum1
Maximum4508
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:32:57.311084image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile220.8
Q11125
median2253
Q33389
95-th percentile4289.2
Maximum4508
Range4507
Interquartile range (IQR)2264

Descriptive statistics

Standard deviation1306.5885
Coefficient of variation (CV)0.57931531
Kurtosis-1.205199
Mean2255.4012
Median Absolute Deviation (MAD)1132
Skewness0.00027367834
Sum9781675
Variance1707173.4
MonotonicityNot monotonic
2025-04-07T21:32:57.443635image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
3049 1
 
< 0.1%
3055 1
 
< 0.1%
3221 1
 
< 0.1%
3129 1
 
< 0.1%
2889 1
 
< 0.1%
3325 1
 
< 0.1%
3220 1
 
< 0.1%
3051 1
 
< 0.1%
2835 1
 
< 0.1%
Other values (4327) 4327
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
4508 1
< 0.1%
4507 1
< 0.1%
4506 1
< 0.1%
4505 1
< 0.1%
4504 1
< 0.1%
4503 1
< 0.1%
4502 1
< 0.1%
4501 1
< 0.1%
4500 1
< 0.1%
4499 1
< 0.1%

Year
Real number (ℝ)

Distinct29
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.2301
Minimum1997
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:32:57.570523image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2003
Q12010
median2016
Q32021
95-th percentile2024
Maximum2025
Range28
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.7909674
Coefficient of variation (CV)0.0033698223
Kurtosis-0.48870048
Mean2015.2301
Median Absolute Deviation (MAD)5
Skewness-0.58806545
Sum8740053
Variance46.117239
MonotonicityIncreasing
2025-04-07T21:32:57.693532image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2024 353
 
8.1%
2020 283
 
6.5%
2023 281
 
6.5%
2019 276
 
6.4%
2021 272
 
6.3%
2013 265
 
6.1%
2022 247
 
5.7%
2018 229
 
5.3%
2014 194
 
4.5%
2012 170
 
3.9%
Other values (19) 1767
40.7%
ValueCountFrequency (%)
1997 34
 
0.8%
1998 26
 
0.6%
1999 32
 
0.7%
2000 42
 
1.0%
2001 29
 
0.7%
2002 46
1.1%
2003 63
1.5%
2004 64
1.5%
2005 74
1.7%
2006 107
2.5%
ValueCountFrequency (%)
2025 47
 
1.1%
2024 353
8.1%
2023 281
6.5%
2022 247
5.7%
2021 272
6.3%
2020 283
6.5%
2019 276
6.4%
2018 229
5.3%
2017 160
3.7%
2016 164
3.8%

Month
Real number (ℝ)

Missing 

Distinct12
Distinct (%)0.3%
Missing48
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean6.536955
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:32:57.812197image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4185073
Coefficient of variation (CV)0.52295103
Kurtosis-1.1799576
Mean6.536955
Median Absolute Deviation (MAD)3
Skewness-0.046723201
Sum28037
Variance11.686192
MonotonicityNot monotonic
2025-04-07T21:32:57.918159image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 407
9.4%
7 401
9.2%
10 397
9.2%
1 383
8.8%
11 374
8.6%
5 344
7.9%
9 344
7.9%
8 341
7.9%
3 337
7.8%
4 327
7.5%
Other values (2) 634
14.6%
ValueCountFrequency (%)
1 383
8.8%
2 315
7.3%
3 337
7.8%
4 327
7.5%
5 344
7.9%
6 407
9.4%
7 401
9.2%
8 341
7.9%
9 344
7.9%
10 397
9.2%
ValueCountFrequency (%)
12 319
7.4%
11 374
8.6%
10 397
9.2%
9 344
7.9%
8 341
7.9%
7 401
9.2%
6 407
9.4%
5 344
7.9%
4 327
7.5%
3 337
7.8%

Day
Real number (ℝ)

Missing 

Distinct31
Distinct (%)0.8%
Missing380
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean15.536265
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:32:58.029546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile29.2
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7189583
Coefficient of variation (CV)0.56120042
Kurtosis-1.1749604
Mean15.536265
Median Absolute Deviation (MAD)7
Skewness0.049622349
Sum61477
Variance76.020234
MonotonicityNot monotonic
2025-04-07T21:32:58.159796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
6 150
 
3.5%
7 150
 
3.5%
11 148
 
3.4%
4 145
 
3.3%
19 144
 
3.3%
8 143
 
3.3%
15 139
 
3.2%
18 138
 
3.2%
25 136
 
3.1%
24 136
 
3.1%
Other values (21) 2528
58.3%
(Missing) 380
 
8.8%
ValueCountFrequency (%)
1 126
2.9%
2 123
2.8%
3 118
2.7%
4 145
3.3%
5 117
2.7%
6 150
3.5%
7 150
3.5%
8 143
3.3%
9 124
2.9%
10 129
3.0%
ValueCountFrequency (%)
31 69
1.6%
30 129
3.0%
29 113
2.6%
28 116
2.7%
27 126
2.9%
26 104
2.4%
25 136
3.1%
24 136
3.1%
23 119
2.7%
22 129
3.0%
Distinct92
Distinct (%)2.1%
Missing33
Missing (%)0.8%
Memory size249.1 KiB
2025-04-07T21:32:58.421810image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters8608
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)0.5%

Sample

1st rowKH
2nd rowRW
3rd rowTJ
4th rowSO
5th rowRW
ValueCountFrequency (%)
af 610
14.2%
ss 583
13.5%
sd 381
 
8.9%
sy 368
 
8.6%
so 315
 
7.3%
cd 233
 
5.4%
ps 158
 
3.7%
cf 142
 
3.3%
ml 126
 
2.9%
pk 113
 
2.6%
Other values (82) 1275
29.6%
2025-04-07T21:32:58.792597image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 2418
28.1%
F 789
 
9.2%
D 697
 
8.1%
A 684
 
7.9%
Y 496
 
5.8%
C 456
 
5.3%
M 376
 
4.4%
O 363
 
4.2%
E 305
 
3.5%
P 299
 
3.5%
Other values (16) 1725
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 2418
28.1%
F 789
 
9.2%
D 697
 
8.1%
A 684
 
7.9%
Y 496
 
5.8%
C 456
 
5.3%
M 376
 
4.4%
O 363
 
4.2%
E 305
 
3.5%
P 299
 
3.5%
Other values (16) 1725
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 2418
28.1%
F 789
 
9.2%
D 697
 
8.1%
A 684
 
7.9%
Y 496
 
5.8%
C 456
 
5.3%
M 376
 
4.4%
O 363
 
4.2%
E 305
 
3.5%
P 299
 
3.5%
Other values (16) 1725
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 2418
28.1%
F 789
 
9.2%
D 697
 
8.1%
A 684
 
7.9%
Y 496
 
5.8%
C 456
 
5.3%
M 376
 
4.4%
O 363
 
4.2%
E 305
 
3.5%
P 299
 
3.5%
Other values (16) 1725
20.0%
Distinct95
Distinct (%)2.2%
Missing5
Missing (%)0.1%
Memory size286.2 KiB
2025-04-07T21:32:59.087788image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length32
Median length24
Mean length10.58241
Min length4

Characters and Unicode

Total characters45843
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)0.5%

Sample

1st rowCambodia
2nd rowRwanda
3rd rowTajikistan
4th rowSomalia
5th rowRwanda
ValueCountFrequency (%)
sudan 964
14.5%
afghanistan 610
 
9.2%
south 588
 
8.8%
republic 513
 
7.7%
arab 388
 
5.8%
syrian 368
 
5.5%
somalia 315
 
4.7%
congo 236
 
3.5%
dr 233
 
3.5%
occupied 158
 
2.4%
Other values (101) 2293
34.4%
2025-04-07T21:32:59.644118image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6056
 
13.2%
n 4306
 
9.4%
i 4012
 
8.8%
u 2398
 
5.2%
2334
 
5.1%
S 2317
 
5.1%
r 2115
 
4.6%
e 2043
 
4.5%
t 1981
 
4.3%
o 1971
 
4.3%
Other values (44) 16310
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45843
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6056
 
13.2%
n 4306
 
9.4%
i 4012
 
8.8%
u 2398
 
5.2%
2334
 
5.1%
S 2317
 
5.1%
r 2115
 
4.6%
e 2043
 
4.5%
t 1981
 
4.3%
o 1971
 
4.3%
Other values (44) 16310
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45843
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6056
 
13.2%
n 4306
 
9.4%
i 4012
 
8.8%
u 2398
 
5.2%
2334
 
5.1%
S 2317
 
5.1%
r 2115
 
4.6%
e 2043
 
4.5%
t 1981
 
4.3%
o 1971
 
4.3%
Other values (44) 16310
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45843
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6056
 
13.2%
n 4306
 
9.4%
i 4012
 
8.8%
u 2398
 
5.2%
2334
 
5.1%
S 2317
 
5.1%
r 2115
 
4.6%
e 2043
 
4.5%
t 1981
 
4.3%
o 1971
 
4.3%
Other values (44) 16310
35.6%

Region
Text

Missing 

Distinct458
Distinct (%)11.5%
Missing367
Missing (%)8.5%
Memory size265.1 KiB
2025-04-07T21:33:00.076979image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length25
Median length21
Mean length8.3712846
Min length3

Characters and Unicode

Total characters33234
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique178 ?
Unique (%)4.5%

Sample

1st rowBanteay Meanchey
2nd rowNorthern
3rd rowLower Juba
4th rowKigali
5th rowGedo
ValueCountFrequency (%)
equatoria 220
 
4.2%
darfur 211
 
4.0%
north 211
 
4.0%
central 183
 
3.5%
kivu 157
 
3.0%
west 147
 
2.8%
south 144
 
2.7%
jonglei 134
 
2.6%
strip 128
 
2.4%
gaza 128
 
2.4%
Other values (469) 3581
68.3%
2025-04-07T21:33:00.630775image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4643
 
14.0%
r 2838
 
8.5%
t 1996
 
6.0%
o 1900
 
5.7%
e 1847
 
5.6%
i 1744
 
5.2%
n 1661
 
5.0%
u 1520
 
4.6%
h 1355
 
4.1%
1276
 
3.8%
Other values (52) 12454
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33234
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4643
 
14.0%
r 2838
 
8.5%
t 1996
 
6.0%
o 1900
 
5.7%
e 1847
 
5.6%
i 1744
 
5.2%
n 1661
 
5.0%
u 1520
 
4.6%
h 1355
 
4.1%
1276
 
3.8%
Other values (52) 12454
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33234
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4643
 
14.0%
r 2838
 
8.5%
t 1996
 
6.0%
o 1900
 
5.7%
e 1847
 
5.6%
i 1744
 
5.2%
n 1661
 
5.0%
u 1520
 
4.6%
h 1355
 
4.1%
1276
 
3.8%
Other values (52) 12454
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33234
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4643
 
14.0%
r 2838
 
8.5%
t 1996
 
6.0%
o 1900
 
5.7%
e 1847
 
5.6%
i 1744
 
5.2%
n 1661
 
5.0%
u 1520
 
4.6%
h 1355
 
4.1%
1276
 
3.8%
Other values (52) 12454
37.5%

District
Text

Missing 

Distinct1150
Distinct (%)31.9%
Missing736
Missing (%)17.0%
Memory size249.7 KiB
2025-04-07T21:33:01.050326image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length23
Median length18
Mean length7.3646209
Min length2

Characters and Unicode

Total characters26520
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique619 ?
Unique (%)17.2%

Sample

1st rowMusanze
2nd rowKismayo
3rd rowKigali
4th rowBaardheere
5th rowMusanze
ValueCountFrequency (%)
juba 98
 
2.2%
banadir 83
 
1.9%
gaza 81
 
1.9%
al 73
 
1.7%
kabul 47
 
1.1%
port 44
 
1.0%
east 44
 
1.0%
au 42
 
1.0%
prince 42
 
1.0%
rubkona 39
 
0.9%
Other values (1240) 3768
86.4%
2025-04-07T21:33:01.586261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4593
17.3%
r 1607
 
6.1%
i 1577
 
5.9%
o 1437
 
5.4%
n 1322
 
5.0%
u 1306
 
4.9%
e 1272
 
4.8%
l 1002
 
3.8%
h 864
 
3.3%
837
 
3.2%
Other values (53) 10703
40.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26520
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4593
17.3%
r 1607
 
6.1%
i 1577
 
5.9%
o 1437
 
5.4%
n 1322
 
5.0%
u 1306
 
4.9%
e 1272
 
4.8%
l 1002
 
3.8%
h 864
 
3.3%
837
 
3.2%
Other values (53) 10703
40.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26520
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4593
17.3%
r 1607
 
6.1%
i 1577
 
5.9%
o 1437
 
5.4%
n 1322
 
5.0%
u 1306
 
4.9%
e 1272
 
4.8%
l 1002
 
3.8%
h 864
 
3.3%
837
 
3.2%
Other values (53) 10703
40.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26520
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4593
17.3%
r 1607
 
6.1%
i 1577
 
5.9%
o 1437
 
5.4%
n 1322
 
5.0%
u 1306
 
4.9%
e 1272
 
4.8%
l 1002
 
3.8%
h 864
 
3.3%
837
 
3.2%
Other values (53) 10703
40.4%

City
Text

Missing 

Distinct2097
Distinct (%)62.6%
Missing988
Missing (%)22.8%
Memory size253.1 KiB
2025-04-07T21:33:01.958189image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length76
Median length57
Mean length10.742311
Min length2

Characters and Unicode

Total characters35976
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1719 ?
Unique (%)51.3%

Sample

1st rowRuhengeri
2nd rowKismayo
3rd rowKigali
4th rowTulo-Burago
5th rowRuhengeri
ValueCountFrequency (%)
road 140
 
2.5%
and 126
 
2.2%
between 124
 
2.2%
camp 105
 
1.9%
al 103
 
1.8%
mogadishu 82
 
1.5%
refugee 69
 
1.2%
juba 67
 
1.2%
near 67
 
1.2%
kabul 44
 
0.8%
Other values (2569) 4708
83.5%
2025-04-07T21:33:02.506992image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5650
 
15.7%
e 2355
 
6.5%
2313
 
6.4%
i 2059
 
5.7%
o 1864
 
5.2%
r 1826
 
5.1%
n 1753
 
4.9%
u 1541
 
4.3%
l 1274
 
3.5%
h 1051
 
2.9%
Other values (66) 14290
39.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35976
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5650
 
15.7%
e 2355
 
6.5%
2313
 
6.4%
i 2059
 
5.7%
o 1864
 
5.2%
r 1826
 
5.1%
n 1753
 
4.9%
u 1541
 
4.3%
l 1274
 
3.5%
h 1051
 
2.9%
Other values (66) 14290
39.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35976
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5650
 
15.7%
e 2355
 
6.5%
2313
 
6.4%
i 2059
 
5.7%
o 1864
 
5.2%
r 1826
 
5.1%
n 1753
 
4.9%
u 1541
 
4.3%
l 1274
 
3.5%
h 1051
 
2.9%
Other values (66) 14290
39.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35976
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5650
 
15.7%
e 2355
 
6.5%
2313
 
6.4%
i 2059
 
5.7%
o 1864
 
5.2%
r 1826
 
5.1%
n 1753
 
4.9%
u 1541
 
4.3%
l 1274
 
3.5%
h 1051
 
2.9%
Other values (66) 14290
39.7%

UN
Real number (ℝ)

Skewed  Zeros 

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40949965
Minimum0
Maximum92
Zeros3423
Zeros (%)78.9%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:02.657999image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum92
Range92
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1025775
Coefficient of variation (CV)5.1345038
Kurtosis949.33731
Mean0.40949965
Median Absolute Deviation (MAD)0
Skewness25.732593
Sum1776
Variance4.4208322
MonotonicityNot monotonic
2025-04-07T21:33:02.755645image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 3423
78.9%
1 678
 
15.6%
2 113
 
2.6%
3 46
 
1.1%
4 23
 
0.5%
5 10
 
0.2%
7 9
 
0.2%
8 7
 
0.2%
6 6
 
0.1%
10 4
 
0.1%
Other values (11) 18
 
0.4%
ValueCountFrequency (%)
0 3423
78.9%
1 678
 
15.6%
2 113
 
2.6%
3 46
 
1.1%
4 23
 
0.5%
5 10
 
0.2%
6 6
 
0.1%
7 9
 
0.2%
8 7
 
0.2%
9 3
 
0.1%
ValueCountFrequency (%)
92 1
 
< 0.1%
46 2
< 0.1%
31 1
 
< 0.1%
23 1
 
< 0.1%
19 1
 
< 0.1%
16 1
 
< 0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
12 4
0.1%
11 2
< 0.1%

INGO
Real number (ℝ)

Zeros 

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.80862347
Minimum0
Maximum49
Zeros2346
Zeros (%)54.1%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:02.878245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum49
Range49
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6483497
Coefficient of variation (CV)2.0384638
Kurtosis201.29035
Mean0.80862347
Median Absolute Deviation (MAD)0
Skewness9.6798226
Sum3507
Variance2.7170566
MonotonicityNot monotonic
2025-04-07T21:33:02.996994image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 2346
54.1%
1 1351
31.2%
2 336
 
7.7%
3 138
 
3.2%
4 69
 
1.6%
5 35
 
0.8%
7 16
 
0.4%
6 12
 
0.3%
8 7
 
0.2%
9 6
 
0.1%
Other values (11) 21
 
0.5%
ValueCountFrequency (%)
0 2346
54.1%
1 1351
31.2%
2 336
 
7.7%
3 138
 
3.2%
4 69
 
1.6%
5 35
 
0.8%
6 12
 
0.3%
7 16
 
0.4%
8 7
 
0.2%
9 6
 
0.1%
ValueCountFrequency (%)
49 1
 
< 0.1%
26 1
 
< 0.1%
20 1
 
< 0.1%
18 1
 
< 0.1%
17 2
< 0.1%
16 1
 
< 0.1%
15 2
< 0.1%
13 1
 
< 0.1%
12 4
0.1%
11 1
 
< 0.1%

ICRC
Real number (ℝ)

Zeros 

Distinct9
Distinct (%)0.2%
Missing9
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.051062847
Minimum0
Maximum8
Zeros4223
Zeros (%)97.4%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:03.089590image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.41956846
Coefficient of variation (CV)8.2167073
Kurtosis155.34862
Mean0.051062847
Median Absolute Deviation (MAD)0
Skewness11.481631
Sum221
Variance0.1760377
MonotonicityNot monotonic
2025-04-07T21:33:03.215341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 4223
97.4%
1 62
 
1.4%
2 14
 
0.3%
3 9
 
0.2%
4 8
 
0.2%
5 5
 
0.1%
6 4
 
0.1%
8 2
 
< 0.1%
7 1
 
< 0.1%
(Missing) 9
 
0.2%
ValueCountFrequency (%)
0 4223
97.4%
1 62
 
1.4%
2 14
 
0.3%
3 9
 
0.2%
4 8
 
0.2%
5 5
 
0.1%
6 4
 
0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 1
 
< 0.1%
6 4
 
0.1%
5 5
 
0.1%
4 8
 
0.2%
3 9
 
0.2%
2 14
 
0.3%
1 62
 
1.4%
0 4223
97.4%

NRCS and IFRC
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)0.3%
Missing9
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.12060998
Minimum0
Maximum19
Zeros4075
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:03.326688image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.78125745
Coefficient of variation (CV)6.4775522
Kurtosis248.94174
Mean0.12060998
Median Absolute Deviation (MAD)0
Skewness13.671971
Sum522
Variance0.61036321
MonotonicityNot monotonic
2025-04-07T21:33:03.441493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 4075
94.0%
1 167
 
3.9%
2 38
 
0.9%
4 20
 
0.5%
3 10
 
0.2%
5 6
 
0.1%
10 2
 
< 0.1%
6 2
 
< 0.1%
14 2
 
< 0.1%
15 1
 
< 0.1%
Other values (5) 5
 
0.1%
(Missing) 9
 
0.2%
ValueCountFrequency (%)
0 4075
94.0%
1 167
 
3.9%
2 38
 
0.9%
3 10
 
0.2%
4 20
 
0.5%
5 6
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 1
 
< 0.1%
15 1
 
< 0.1%
14 2
 
< 0.1%
11 1
 
< 0.1%
10 2
 
< 0.1%
9 1
 
< 0.1%
7 1
 
< 0.1%
6 2
 
< 0.1%
5 6
0.1%

NNGO
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)0.3%
Missing9
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.47481516
Minimum0
Maximum15
Zeros3241
Zeros (%)74.7%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:03.552321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1905621
Coefficient of variation (CV)2.5074223
Kurtosis32.761848
Mean0.47481516
Median Absolute Deviation (MAD)0
Skewness4.7344688
Sum2055
Variance1.4174381
MonotonicityNot monotonic
2025-04-07T21:33:03.658896image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 3241
74.7%
1 680
 
15.7%
2 189
 
4.4%
3 97
 
2.2%
4 43
 
1.0%
5 30
 
0.7%
6 19
 
0.4%
8 8
 
0.2%
7 8
 
0.2%
12 3
 
0.1%
Other values (5) 10
 
0.2%
(Missing) 9
 
0.2%
ValueCountFrequency (%)
0 3241
74.7%
1 680
 
15.7%
2 189
 
4.4%
3 97
 
2.2%
4 43
 
1.0%
5 30
 
0.7%
6 19
 
0.4%
7 8
 
0.2%
8 8
 
0.2%
9 2
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
14 2
 
< 0.1%
12 3
 
0.1%
11 3
 
0.1%
10 2
 
< 0.1%
9 2
 
< 0.1%
8 8
 
0.2%
7 8
 
0.2%
6 19
0.4%
5 30
0.7%

Other
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.024210284
Minimum0
Maximum5
Zeros4270
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:03.781022image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.22343032
Coefficient of variation (CV)9.2287361
Kurtosis179.8719
Mean0.024210284
Median Absolute Deviation (MAD)0
Skewness12.147399
Sum105
Variance0.049921107
MonotonicityNot monotonic
2025-04-07T21:33:03.884026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 4270
98.5%
1 42
 
1.0%
2 17
 
0.4%
3 4
 
0.1%
4 3
 
0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 4270
98.5%
1 42
 
1.0%
2 17
 
0.4%
3 4
 
0.1%
4 3
 
0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 3
 
0.1%
3 4
 
0.1%
2 17
 
0.4%
1 42
 
1.0%
0 4270
98.5%

Nationals killed
Real number (ℝ)

Zeros 

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64676043
Minimum0
Maximum70
Zeros2590
Zeros (%)59.7%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:03.986099image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum70
Range70
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7675606
Coefficient of variation (CV)2.7329449
Kurtosis635.73098
Mean0.64676043
Median Absolute Deviation (MAD)0
Skewness19.361185
Sum2805
Variance3.1242705
MonotonicityNot monotonic
2025-04-07T21:33:04.100672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 2590
59.7%
1 1357
31.3%
2 200
 
4.6%
3 80
 
1.8%
4 39
 
0.9%
5 19
 
0.4%
6 15
 
0.3%
7 7
 
0.2%
10 7
 
0.2%
9 7
 
0.2%
Other values (7) 16
 
0.4%
ValueCountFrequency (%)
0 2590
59.7%
1 1357
31.3%
2 200
 
4.6%
3 80
 
1.8%
4 39
 
0.9%
5 19
 
0.4%
6 15
 
0.3%
7 7
 
0.2%
8 6
 
0.1%
9 7
 
0.2%
ValueCountFrequency (%)
70 1
 
< 0.1%
41 1
 
< 0.1%
31 1
 
< 0.1%
17 1
 
< 0.1%
14 3
0.1%
12 3
0.1%
10 7
0.2%
9 7
0.2%
8 6
0.1%
7 7
0.2%

Nationals wounded
Real number (ℝ)

Zeros 

Distinct19
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62808393
Minimum0
Maximum37
Zeros2619
Zeros (%)60.4%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:04.216987image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum37
Range37
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4613012
Coefficient of variation (CV)2.3266018
Kurtosis201.3627
Mean0.62808393
Median Absolute Deviation (MAD)0
Skewness10.815434
Sum2724
Variance2.1354011
MonotonicityNot monotonic
2025-04-07T21:33:04.334437image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 2619
60.4%
1 1280
29.5%
2 261
 
6.0%
3 80
 
1.8%
4 35
 
0.8%
5 24
 
0.6%
6 10
 
0.2%
8 7
 
0.2%
7 6
 
0.1%
10 3
 
0.1%
Other values (9) 12
 
0.3%
ValueCountFrequency (%)
0 2619
60.4%
1 1280
29.5%
2 261
 
6.0%
3 80
 
1.8%
4 35
 
0.8%
5 24
 
0.6%
6 10
 
0.2%
7 6
 
0.1%
8 7
 
0.2%
10 3
 
0.1%
ValueCountFrequency (%)
37 1
 
< 0.1%
35 1
 
< 0.1%
22 2
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
16 1
 
< 0.1%
15 2
< 0.1%
13 1
 
< 0.1%
11 2
< 0.1%
10 3
0.1%

Nationals kidnapped
Real number (ℝ)

Zeros 

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40880793
Minimum0
Maximum19
Zeros3556
Zeros (%)82.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:04.450818image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2395489
Coefficient of variation (CV)3.0321058
Kurtosis47.086463
Mean0.40880793
Median Absolute Deviation (MAD)0
Skewness5.6089629
Sum1773
Variance1.5364814
MonotonicityNot monotonic
2025-04-07T21:33:04.558294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 3556
82.0%
1 384
 
8.9%
2 168
 
3.9%
3 103
 
2.4%
4 51
 
1.2%
5 32
 
0.7%
7 11
 
0.3%
6 10
 
0.2%
8 5
 
0.1%
9 4
 
0.1%
Other values (7) 13
 
0.3%
ValueCountFrequency (%)
0 3556
82.0%
1 384
 
8.9%
2 168
 
3.9%
3 103
 
2.4%
4 51
 
1.2%
5 32
 
0.7%
6 10
 
0.2%
7 11
 
0.3%
8 5
 
0.1%
9 4
 
0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
16 1
 
< 0.1%
15 2
 
< 0.1%
14 2
 
< 0.1%
12 1
 
< 0.1%
11 2
 
< 0.1%
10 4
 
0.1%
9 4
 
0.1%
8 5
0.1%
7 11
0.3%

Total nationals
Real number (ℝ)

Zeros 

Distinct26
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6836523
Minimum0
Maximum92
Zeros378
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:04.690202image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile5
Maximum92
Range92
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.6087054
Coefficient of variation (CV)1.5494324
Kurtosis403.78407
Mean1.6836523
Median Absolute Deviation (MAD)0
Skewness15.019444
Sum7302
Variance6.8053439
MonotonicityNot monotonic
2025-04-07T21:33:04.806919image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 2702
62.3%
2 616
 
14.2%
0 378
 
8.7%
3 274
 
6.3%
4 132
 
3.0%
5 76
 
1.8%
6 37
 
0.9%
7 33
 
0.8%
8 23
 
0.5%
10 17
 
0.4%
Other values (16) 49
 
1.1%
ValueCountFrequency (%)
0 378
 
8.7%
1 2702
62.3%
2 616
 
14.2%
3 274
 
6.3%
4 132
 
3.0%
5 76
 
1.8%
6 37
 
0.9%
7 33
 
0.8%
8 23
 
0.5%
9 13
 
0.3%
ValueCountFrequency (%)
92 1
< 0.1%
49 1
< 0.1%
46 2
< 0.1%
31 1
< 0.1%
26 1
< 0.1%
23 1
< 0.1%
19 2
< 0.1%
18 2
< 0.1%
17 1
< 0.1%
16 1
< 0.1%

Internationals killed
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.055798939
Minimum0
Maximum11
Zeros4161
Zeros (%)95.9%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:04.906343image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.36017647
Coefficient of variation (CV)6.4548981
Kurtosis303.25074
Mean0.055798939
Median Absolute Deviation (MAD)0
Skewness13.911219
Sum242
Variance0.12972709
MonotonicityNot monotonic
2025-04-07T21:33:05.008240image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 4161
95.9%
1 146
 
3.4%
2 15
 
0.3%
3 9
 
0.2%
4 2
 
< 0.1%
6 2
 
< 0.1%
11 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 4161
95.9%
1 146
 
3.4%
2 15
 
0.3%
3 9
 
0.2%
4 2
 
< 0.1%
6 2
 
< 0.1%
8 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
8 1
 
< 0.1%
6 2
 
< 0.1%
4 2
 
< 0.1%
3 9
 
0.2%
2 15
 
0.3%
1 146
 
3.4%
0 4161
95.9%

Internationals wounded
Real number (ℝ)

Zeros 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.064330182
Minimum0
Maximum15
Zeros4148
Zeros (%)95.6%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:05.107957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.43884595
Coefficient of variation (CV)6.8217737
Kurtosis435.817
Mean0.064330182
Median Absolute Deviation (MAD)0
Skewness17.010254
Sum279
Variance0.19258577
MonotonicityNot monotonic
2025-04-07T21:33:05.224451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 4148
95.6%
1 148
 
3.4%
2 27
 
0.6%
3 8
 
0.2%
9 2
 
< 0.1%
5 1
 
< 0.1%
8 1
 
< 0.1%
15 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 4148
95.6%
1 148
 
3.4%
2 27
 
0.6%
3 8
 
0.2%
5 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
9 2
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%
3 8
 
0.2%
2 27
 
0.6%
1 148
 
3.4%
0 4148
95.6%

Internationals kidnapped
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.083698409
Minimum0
Maximum12
Zeros4136
Zeros (%)95.4%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:05.340871image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.50701206
Coefficient of variation (CV)6.0576069
Kurtosis170.52832
Mean0.083698409
Median Absolute Deviation (MAD)0
Skewness10.966504
Sum363
Variance0.25706123
MonotonicityNot monotonic
2025-04-07T21:33:05.457182image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 4136
95.4%
1 119
 
2.7%
2 52
 
1.2%
3 15
 
0.3%
4 4
 
0.1%
5 3
 
0.1%
9 3
 
0.1%
6 3
 
0.1%
7 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
0 4136
95.4%
1 119
 
2.7%
2 52
 
1.2%
3 15
 
0.3%
4 4
 
0.1%
5 3
 
0.1%
6 3
 
0.1%
7 1
 
< 0.1%
9 3
 
0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
9 3
 
0.1%
7 1
 
< 0.1%
6 3
 
0.1%
5 3
 
0.1%
4 4
 
0.1%
3 15
 
0.3%
2 52
 
1.2%
1 119
 
2.7%
0 4136
95.4%

Total internationals
Real number (ℝ)

Zeros 

Distinct14
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20382753
Minimum0
Maximum15
Zeros3799
Zeros (%)87.6%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:05.557763image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.77044945
Coefficient of variation (CV)3.7799087
Kurtosis98.606251
Mean0.20382753
Median Absolute Deviation (MAD)0
Skewness8.1154249
Sum884
Variance0.59359236
MonotonicityNot monotonic
2025-04-07T21:33:05.673807image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 3799
87.6%
1 374
 
8.6%
2 100
 
2.3%
3 31
 
0.7%
4 11
 
0.3%
5 5
 
0.1%
6 5
 
0.1%
9 4
 
0.1%
12 2
 
< 0.1%
7 2
 
< 0.1%
Other values (4) 4
 
0.1%
ValueCountFrequency (%)
0 3799
87.6%
1 374
 
8.6%
2 100
 
2.3%
3 31
 
0.7%
4 11
 
0.3%
5 5
 
0.1%
6 5
 
0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 4
 
0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
12 2
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 4
 
0.1%
8 1
 
< 0.1%
7 2
 
< 0.1%
6 5
0.1%
5 5
0.1%
4 11
0.3%

Total killed
Real number (ℝ)

Zeros 

Distinct18
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70255937
Minimum0
Maximum70
Zeros2452
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:05.906811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum70
Range70
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8138905
Coefficient of variation (CV)2.5818323
Kurtosis574.01723
Mean0.70255937
Median Absolute Deviation (MAD)0
Skewness18.19485
Sum3047
Variance3.2901987
MonotonicityNot monotonic
2025-04-07T21:33:06.021956image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 2452
56.5%
1 1455
33.5%
2 224
 
5.2%
3 86
 
2.0%
4 40
 
0.9%
5 21
 
0.5%
6 17
 
0.4%
8 9
 
0.2%
7 8
 
0.2%
10 8
 
0.2%
Other values (8) 17
 
0.4%
ValueCountFrequency (%)
0 2452
56.5%
1 1455
33.5%
2 224
 
5.2%
3 86
 
2.0%
4 40
 
0.9%
5 21
 
0.5%
6 17
 
0.4%
7 8
 
0.2%
8 9
 
0.2%
9 6
 
0.1%
ValueCountFrequency (%)
70 1
 
< 0.1%
41 1
 
< 0.1%
31 1
 
< 0.1%
20 1
 
< 0.1%
17 1
 
< 0.1%
14 3
 
0.1%
12 3
 
0.1%
10 8
0.2%
9 6
0.1%
8 9
0.2%

Total wounded
Real number (ℝ)

Zeros 

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69241411
Minimum0
Maximum37
Zeros2464
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:06.137676image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum37
Range37
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5145116
Coefficient of variation (CV)2.1872917
Kurtosis176.37089
Mean0.69241411
Median Absolute Deviation (MAD)0
Skewness10.061812
Sum3003
Variance2.2937455
MonotonicityNot monotonic
2025-04-07T21:33:06.254894image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 2464
56.8%
1 1382
31.9%
2 289
 
6.7%
3 93
 
2.1%
4 41
 
0.9%
5 24
 
0.6%
6 10
 
0.2%
8 9
 
0.2%
7 7
 
0.2%
10 3
 
0.1%
Other values (11) 15
 
0.3%
ValueCountFrequency (%)
0 2464
56.8%
1 1382
31.9%
2 289
 
6.7%
3 93
 
2.1%
4 41
 
0.9%
5 24
 
0.6%
6 10
 
0.2%
7 7
 
0.2%
8 9
 
0.2%
9 2
 
< 0.1%
ValueCountFrequency (%)
37 1
< 0.1%
35 1
< 0.1%
22 2
< 0.1%
19 1
< 0.1%
18 1
< 0.1%
17 1
< 0.1%
16 1
< 0.1%
15 2
< 0.1%
13 1
< 0.1%
11 2
< 0.1%

Total kidnapped
Real number (ℝ)

Zeros 

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49250634
Minimum0
Maximum20
Zeros3416
Zeros (%)78.8%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:06.382195image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3630609
Coefficient of variation (CV)2.7676008
Kurtosis39.299453
Mean0.49250634
Median Absolute Deviation (MAD)0
Skewness5.1303021
Sum2136
Variance1.8579351
MonotonicityNot monotonic
2025-04-07T21:33:06.488124image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 3416
78.8%
1 439
 
10.1%
2 204
 
4.7%
3 126
 
2.9%
4 59
 
1.4%
5 37
 
0.9%
6 15
 
0.3%
7 14
 
0.3%
10 6
 
0.1%
8 6
 
0.1%
Other values (7) 15
 
0.3%
ValueCountFrequency (%)
0 3416
78.8%
1 439
 
10.1%
2 204
 
4.7%
3 126
 
2.9%
4 59
 
1.4%
5 37
 
0.9%
6 15
 
0.3%
7 14
 
0.3%
8 6
 
0.1%
9 3
 
0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
16 1
 
< 0.1%
15 3
 
0.1%
14 2
 
< 0.1%
12 3
 
0.1%
11 2
 
< 0.1%
10 6
0.1%
9 3
 
0.1%
8 6
0.1%
7 14
0.3%

Total affected
Real number (ℝ)

Distinct27
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8874798
Minimum0
Maximum92
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:06.619618image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum92
Range92
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.6596223
Coefficient of variation (CV)1.4090865
Kurtosis370.57071
Mean1.8874798
Median Absolute Deviation (MAD)0
Skewness14.259824
Sum8186
Variance7.0735909
MonotonicityNot monotonic
2025-04-07T21:33:06.743595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 2892
66.7%
2 704
 
16.2%
3 310
 
7.1%
4 156
 
3.6%
5 86
 
2.0%
6 47
 
1.1%
7 37
 
0.9%
8 27
 
0.6%
10 18
 
0.4%
9 14
 
0.3%
Other values (17) 46
 
1.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 2892
66.7%
2 704
 
16.2%
3 310
 
7.1%
4 156
 
3.6%
5 86
 
2.0%
6 47
 
1.1%
7 37
 
0.9%
8 27
 
0.6%
9 14
 
0.3%
ValueCountFrequency (%)
92 1
< 0.1%
49 1
< 0.1%
46 2
< 0.1%
31 1
< 0.1%
26 1
< 0.1%
23 1
< 0.1%
20 2
< 0.1%
19 1
< 0.1%
18 2
< 0.1%
17 2
< 0.1%

Gender Male
Real number (ℝ)

Zeros 

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88978557
Minimum0
Maximum17
Zeros1669
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:06.835800image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2159872
Coefficient of variation (CV)1.366607
Kurtosis35.231487
Mean0.88978557
Median Absolute Deviation (MAD)1
Skewness4.5055802
Sum3859
Variance1.4786249
MonotonicityNot monotonic
2025-04-07T21:33:06.967190image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 2086
48.1%
0 1669
38.5%
2 347
 
8.0%
3 111
 
2.6%
4 46
 
1.1%
5 27
 
0.6%
6 16
 
0.4%
7 10
 
0.2%
8 8
 
0.2%
9 5
 
0.1%
Other values (7) 12
 
0.3%
ValueCountFrequency (%)
0 1669
38.5%
1 2086
48.1%
2 347
 
8.0%
3 111
 
2.6%
4 46
 
1.1%
5 27
 
0.6%
6 16
 
0.4%
7 10
 
0.2%
8 8
 
0.2%
9 5
 
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
< 0.1%
13 1
 
< 0.1%
12 2
 
< 0.1%
11 4
 
0.1%
10 1
 
< 0.1%
9 5
0.1%
8 8
0.2%
7 10
0.2%

Gender Female
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13880563
Minimum0
Maximum7
Zeros3864
Zeros (%)89.1%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:07.073669image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.47064154
Coefficient of variation (CV)3.3906518
Kurtosis50.883195
Mean0.13880563
Median Absolute Deviation (MAD)0
Skewness5.6272226
Sum602
Variance0.22150346
MonotonicityNot monotonic
2025-04-07T21:33:07.178808image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 3864
89.1%
1 392
 
9.0%
2 55
 
1.3%
3 16
 
0.4%
4 5
 
0.1%
7 3
 
0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 3864
89.1%
1 392
 
9.0%
2 55
 
1.3%
3 16
 
0.4%
4 5
 
0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 3
 
0.1%
ValueCountFrequency (%)
7 3
 
0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
4 5
 
0.1%
3 16
 
0.4%
2 55
 
1.3%
1 392
 
9.0%
0 3864
89.1%

Gender Unknown
Real number (ℝ)

Zeros 

Distinct25
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.85842748
Minimum0
Maximum92
Zeros2834
Zeros (%)65.3%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2025-04-07T21:33:07.295539image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum92
Range92
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.524834
Coefficient of variation (CV)2.9412315
Kurtosis445.41061
Mean0.85842748
Median Absolute Deviation (MAD)0
Skewness15.525967
Sum3723
Variance6.3747865
MonotonicityNot monotonic
2025-04-07T21:33:07.409118image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 2834
65.3%
1 761
 
17.5%
2 337
 
7.8%
3 158
 
3.6%
4 85
 
2.0%
5 48
 
1.1%
6 29
 
0.7%
8 18
 
0.4%
7 18
 
0.4%
10 12
 
0.3%
Other values (15) 37
 
0.9%
ValueCountFrequency (%)
0 2834
65.3%
1 761
 
17.5%
2 337
 
7.8%
3 158
 
3.6%
4 85
 
2.0%
5 48
 
1.1%
6 29
 
0.7%
7 18
 
0.4%
8 18
 
0.4%
9 8
 
0.2%
ValueCountFrequency (%)
92 1
 
< 0.1%
46 1
 
< 0.1%
38 1
 
< 0.1%
35 1
 
< 0.1%
31 1
 
< 0.1%
26 1
 
< 0.1%
23 1
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
17 3
0.1%

Means of attack
Categorical

Distinct14
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size286.9 KiB
Shooting
1240 
Kidnapping
890 
Bodily assault
836 
Unknown
510 
Aerial bombardment
263 
Other values (9)
598 

Length

Max length20
Median length18
Mean length10.710399
Min length7

Characters and Unicode

Total characters46451
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowShooting
3rd rowKidnapping
4th rowUnknown
5th rowShooting

Common Values

ValueCountFrequency (%)
Shooting 1240
28.6%
Kidnapping 890
20.5%
Bodily assault 836
19.3%
Unknown 510
11.8%
Aerial bombardment 263
 
6.1%
Shelling 157
 
3.6%
Kidnap-killing 111
 
2.6%
Vehicle-born IED 76
 
1.8%
Roadside IED 65
 
1.5%
Rape/sexual assault 46
 
1.1%
Other values (4) 143
 
3.3%

Length

2025-04-07T21:33:07.523427image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
shooting 1240
21.7%
kidnapping 890
15.5%
assault 882
15.4%
bodily 836
14.6%
unknown 510
8.9%
aerial 263
 
4.6%
bombardment 263
 
4.6%
ied 161
 
2.8%
shelling 157
 
2.7%
kidnap-killing 111
 
1.9%
Other values (9) 414
 
7.2%

Most occurring characters

ValueCountFrequency (%)
n 5366
 
11.6%
i 4834
 
10.4%
o 4354
 
9.4%
a 3565
 
7.7%
l 2723
 
5.9%
t 2508
 
5.4%
g 2398
 
5.2%
d 2289
 
4.9%
p 2021
 
4.4%
s 1965
 
4.2%
Other values (28) 14428
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 5366
 
11.6%
i 4834
 
10.4%
o 4354
 
9.4%
a 3565
 
7.7%
l 2723
 
5.9%
t 2508
 
5.4%
g 2398
 
5.2%
d 2289
 
4.9%
p 2021
 
4.4%
s 1965
 
4.2%
Other values (28) 14428
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 5366
 
11.6%
i 4834
 
10.4%
o 4354
 
9.4%
a 3565
 
7.7%
l 2723
 
5.9%
t 2508
 
5.4%
g 2398
 
5.2%
d 2289
 
4.9%
p 2021
 
4.4%
s 1965
 
4.2%
Other values (28) 14428
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 5366
 
11.6%
i 4834
 
10.4%
o 4354
 
9.4%
a 3565
 
7.7%
l 2723
 
5.9%
t 2508
 
5.4%
g 2398
 
5.2%
d 2289
 
4.9%
p 2021
 
4.4%
s 1965
 
4.2%
Other values (28) 14428
31.1%

Attack context
Categorical

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size283.3 KiB
Ambush
1400 
Individual attack
873 
Unknown
734 
Combat/Crossfire
668 
Raid
507 
Other values (2)
155 

Length

Max length17
Median length16
Mean length9.8621167
Min length4

Characters and Unicode

Total characters42772
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowRaid
3rd rowUnknown
4th rowUnknown
5th rowIndividual attack

Common Values

ValueCountFrequency (%)
Ambush 1400
32.3%
Individual attack 873
20.1%
Unknown 734
16.9%
Combat/Crossfire 668
15.4%
Raid 507
 
11.7%
Mob violence 94
 
2.2%
Detention 61
 
1.4%

Length

2025-04-07T21:33:07.654614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T21:33:07.803258image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
ambush 1400
26.4%
individual 873
16.5%
attack 873
16.5%
unknown 734
13.8%
combat/crossfire 668
12.6%
raid 507
 
9.6%
mob 94
 
1.8%
violence 94
 
1.8%
detention 61
 
1.2%

Most occurring characters

ValueCountFrequency (%)
a 3794
 
8.9%
n 3291
 
7.7%
i 3076
 
7.2%
s 2736
 
6.4%
t 2536
 
5.9%
o 2319
 
5.4%
u 2273
 
5.3%
d 2253
 
5.3%
b 2162
 
5.1%
m 2068
 
4.8%
Other values (18) 16264
38.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42772
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3794
 
8.9%
n 3291
 
7.7%
i 3076
 
7.2%
s 2736
 
6.4%
t 2536
 
5.9%
o 2319
 
5.4%
u 2273
 
5.3%
d 2253
 
5.3%
b 2162
 
5.1%
m 2068
 
4.8%
Other values (18) 16264
38.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42772
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3794
 
8.9%
n 3291
 
7.7%
i 3076
 
7.2%
s 2736
 
6.4%
t 2536
 
5.9%
o 2319
 
5.4%
u 2273
 
5.3%
d 2253
 
5.3%
b 2162
 
5.1%
m 2068
 
4.8%
Other values (18) 16264
38.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42772
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3794
 
8.9%
n 3291
 
7.7%
i 3076
 
7.2%
s 2736
 
6.4%
t 2536
 
5.9%
o 2319
 
5.4%
u 2273
 
5.3%
d 2253
 
5.3%
b 2162
 
5.1%
m 2068
 
4.8%
Other values (18) 16264
38.0%

Location
Categorical

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size275.3 KiB
Road
1530 
Unknown
982 
Public location
565 
Project site
524 
Office/compound
339 
Other values (2)
397 

Length

Max length15
Median length12
Mean length7.9787872
Min length4

Characters and Unicode

Total characters34604
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowOffice/compound
3rd rowUnknown
4th rowUnknown
5th rowUnknown

Common Values

ValueCountFrequency (%)
Road 1530
35.3%
Unknown 982
22.6%
Public location 565
 
13.0%
Project site 524
 
12.1%
Office/compound 339
 
7.8%
Home 339
 
7.8%
Custody 58
 
1.3%

Length

2025-04-07T21:33:07.954537image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T21:33:08.074368image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
road 1530
28.2%
unknown 982
18.1%
public 565
 
10.4%
location 565
 
10.4%
project 524
 
9.7%
site 524
 
9.7%
office/compound 339
 
6.2%
home 339
 
6.2%
custody 58
 
1.1%

Most occurring characters

ValueCountFrequency (%)
o 5241
15.1%
n 3850
 
11.1%
c 2332
 
6.7%
a 2095
 
6.1%
i 1993
 
5.8%
d 1927
 
5.6%
e 1726
 
5.0%
t 1671
 
4.8%
R 1530
 
4.4%
l 1130
 
3.3%
Other values (18) 11109
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34604
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 5241
15.1%
n 3850
 
11.1%
c 2332
 
6.7%
a 2095
 
6.1%
i 1993
 
5.8%
d 1927
 
5.6%
e 1726
 
5.0%
t 1671
 
4.8%
R 1530
 
4.4%
l 1130
 
3.3%
Other values (18) 11109
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34604
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 5241
15.1%
n 3850
 
11.1%
c 2332
 
6.7%
a 2095
 
6.1%
i 1993
 
5.8%
d 1927
 
5.6%
e 1726
 
5.0%
t 1671
 
4.8%
R 1530
 
4.4%
l 1130
 
3.3%
Other values (18) 11109
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34604
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 5241
15.1%
n 3850
 
11.1%
c 2332
 
6.7%
a 2095
 
6.1%
i 1993
 
5.8%
d 1927
 
5.6%
e 1726
 
5.0%
t 1671
 
4.8%
R 1530
 
4.4%
l 1130
 
3.3%
Other values (18) 11109
32.1%

Latitude
Real number (ℝ)

Distinct2824
Distinct (%)65.3%
Missing13
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean16.774788
Minimum-34.883611
Maximum52.253183
Zeros0
Zeros (%)0.0%
Negative379
Negative (%)8.7%
Memory size34.0 KiB
2025-04-07T21:33:08.236890image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-34.883611
5-th percentile-3.105795
Q15.915749
median13.4435
Q333.095579
95-th percentile36.3755
Maximum52.253183
Range87.136794
Interquartile range (IQR)27.17983

Descriptive statistics

Standard deviation14.495858
Coefficient of variation (CV)0.86414556
Kurtosis-0.74939924
Mean16.774788
Median Absolute Deviation (MAD)10.43992
Skewness0.071816765
Sum72534.181
Variance210.12991
MonotonicityNot monotonic
2025-04-07T21:33:08.376382image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.329444 47
 
1.1%
2.033333 39
 
0.9%
14.5844444 34
 
0.8%
33.93911 32
 
0.7%
33.509147 27
 
0.6%
8.3676771 23
 
0.5%
16 21
 
0.5%
18.533333 21
 
0.5%
33.0955793 20
 
0.5%
13.4263009 17
 
0.4%
Other values (2814) 4043
93.2%
ValueCountFrequency (%)
-34.883611 1
< 0.1%
-34.603803 1
< 0.1%
-33.483825 1
< 0.1%
-33.45 1
< 0.1%
-29.6039267 1
< 0.1%
-29.31 2
< 0.1%
-28.8166236 2
< 0.1%
-27.116667 1
< 0.1%
-26.204444 1
< 0.1%
-25.9662133 2
< 0.1%
ValueCountFrequency (%)
52.253183 1
< 0.1%
52.23 1
< 0.1%
51.997222 1
< 0.1%
51.493889 2
< 0.1%
50.92681 1
< 0.1%
50.450693 1
< 0.1%
50.45 2
< 0.1%
50.432922 1
< 0.1%
50.279954 1
< 0.1%
50.113541 1
< 0.1%

Longitude
Real number (ℝ)

Distinct2854
Distinct (%)66.0%
Missing13
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean36.511102
Minimum-102.28333
Maximum179.01227
Zeros0
Zeros (%)0.0%
Negative306
Negative (%)7.1%
Memory size34.0 KiB
2025-04-07T21:33:08.506270image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-102.28333
5-th percentile-3.9952411
Q128.75
median34.45525
Q345.377398
95-th percentile73.183972
Maximum179.01227
Range281.29561
Interquartile range (IQR)16.627398

Descriptive statistics

Standard deviation30.006646
Coefficient of variation (CV)0.82184992
Kurtosis5.1907357
Mean36.511102
Median Absolute Deviation (MAD)9.6144719
Skewness-1.0205364
Sum157874
Variance900.39881
MonotonicityNot monotonic
2025-04-07T21:33:08.637995image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.08565623 47
 
1.1%
45.35 35
 
0.8%
29.4917691 34
 
0.8%
67.709953 32
 
0.7%
36.306485 27
 
0.6%
49.083416 23
 
0.5%
-72.333333 21
 
0.5%
30 20
 
0.5%
44.1749775 20
 
0.5%
22.6574884 17
 
0.4%
Other values (2844) 4048
93.3%
ValueCountFrequency (%)
-102.283333 1
< 0.1%
-101.551111 1
< 0.1%
-99.692483 1
< 0.1%
-99.133333 1
< 0.1%
-98.907978 1
< 0.1%
-96.645278 1
< 0.1%
-91.183333 1
< 0.1%
-91.16233009 1
< 0.1%
-90.535278 2
< 0.1%
-90.506882 1
< 0.1%
ValueCountFrequency (%)
179.0122737 1
< 0.1%
179 1
< 0.1%
178.4421662 2
< 0.1%
147.199722 1
< 0.1%
147.1498139 1
< 0.1%
147.149444 1
< 0.1%
144.2489081 1
< 0.1%
143.586538 1
< 0.1%
143.420306 1
< 0.1%
142.572 1
< 0.1%

Motive
Categorical

Distinct6
Distinct (%)0.1%
Missing4
Missing (%)0.1%
Memory size275.7 KiB
Unknown
1946 
Political
813 
Incidental
782 
Economic
650 
Disputed
 
120

Length

Max length10
Median length9
Mean length8.0842372
Min length5

Characters and Unicode

Total characters35029
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowPolitical
4th rowPolitical
5th rowIncidental

Common Values

ValueCountFrequency (%)
Unknown 1946
44.9%
Political 813
18.7%
Incidental 782
18.0%
Economic 650
 
15.0%
Disputed 120
 
2.8%
Other 22
 
0.5%
(Missing) 4
 
0.1%

Length

2025-04-07T21:33:08.769364image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T21:33:08.871378image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
unknown 1946
44.9%
political 813
18.8%
incidental 782
18.0%
economic 650
 
15.0%
disputed 120
 
2.8%
other 22
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n 8052
23.0%
o 4059
11.6%
i 3178
 
9.1%
c 2895
 
8.3%
l 2408
 
6.9%
U 1946
 
5.6%
k 1946
 
5.6%
w 1946
 
5.6%
t 1737
 
5.0%
a 1595
 
4.6%
Other values (13) 5267
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35029
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 8052
23.0%
o 4059
11.6%
i 3178
 
9.1%
c 2895
 
8.3%
l 2408
 
6.9%
U 1946
 
5.6%
k 1946
 
5.6%
w 1946
 
5.6%
t 1737
 
5.0%
a 1595
 
4.6%
Other values (13) 5267
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35029
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 8052
23.0%
o 4059
11.6%
i 3178
 
9.1%
c 2895
 
8.3%
l 2408
 
6.9%
U 1946
 
5.6%
k 1946
 
5.6%
w 1946
 
5.6%
t 1737
 
5.0%
a 1595
 
4.6%
Other values (13) 5267
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35029
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 8052
23.0%
o 4059
11.6%
i 3178
 
9.1%
c 2895
 
8.3%
l 2408
 
6.9%
U 1946
 
5.6%
k 1946
 
5.6%
w 1946
 
5.6%
t 1737
 
5.0%
a 1595
 
4.6%
Other values (13) 5267
15.0%

Actor type
Categorical

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size304.5 KiB
Unknown
2274 
Non-state armed group: Unknown
375 
Non-state armed group: National
352 
Unaffiliated
 
223
Foreign or coalition forces
 
201
Other values (11)
912 

Length

Max length34
Median length7
Mean length14.854969
Min length7

Characters and Unicode

Total characters64426
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowNon-state armed group: Regional
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown 2274
52.4%
Non-state armed group: Unknown 375
 
8.6%
Non-state armed group: National 352
 
8.1%
Unaffiliated 223
 
5.1%
Foreign or coalition forces 201
 
4.6%
Host state 192
 
4.4%
Non-state armed group: Regional 185
 
4.3%
Non-state armed group: Subnational 146
 
3.4%
Criminal 132
 
3.0%
Police or paramilitary 88
 
2.0%
Other values (6) 169
 
3.9%

Length

2025-04-07T21:33:09.028507image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
unknown 2661
30.5%
armed 1091
12.5%
group 1091
12.5%
non-state 1091
12.5%
national 352
 
4.0%
or 289
 
3.3%
state 231
 
2.6%
unaffiliated 223
 
2.6%
host 219
 
2.5%
foreign 201
 
2.3%
Other values (12) 1268
14.5%

Most occurring characters

ValueCountFrequency (%)
n 10722
16.6%
o 6959
 
10.8%
a 4705
 
7.3%
4391
 
6.8%
t 3970
 
6.2%
e 3505
 
5.4%
r 3278
 
5.1%
U 2872
 
4.5%
w 2661
 
4.1%
k 2661
 
4.1%
Other values (23) 18702
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 10722
16.6%
o 6959
 
10.8%
a 4705
 
7.3%
4391
 
6.8%
t 3970
 
6.2%
e 3505
 
5.4%
r 3278
 
5.1%
U 2872
 
4.5%
w 2661
 
4.1%
k 2661
 
4.1%
Other values (23) 18702
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 10722
16.6%
o 6959
 
10.8%
a 4705
 
7.3%
4391
 
6.8%
t 3970
 
6.2%
e 3505
 
5.4%
r 3278
 
5.1%
U 2872
 
4.5%
w 2661
 
4.1%
k 2661
 
4.1%
Other values (23) 18702
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 10722
16.6%
o 6959
 
10.8%
a 4705
 
7.3%
4391
 
6.8%
t 3970
 
6.2%
e 3505
 
5.4%
r 3278
 
5.1%
U 2872
 
4.5%
w 2661
 
4.1%
k 2661
 
4.1%
Other values (23) 18702
29.0%
Distinct280
Distinct (%)6.5%
Missing8
Missing (%)0.2%
Memory size292.1 KiB
2025-04-07T21:33:09.419784image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length107
Median length7
Mean length11.976438
Min length3

Characters and Unicode

Total characters51846
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170 ?
Unique (%)3.9%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowAl-Itihaad al-Islamiya
5th rowUnknown
ValueCountFrequency (%)
unknown 2463
33.3%
not 597
 
8.1%
applicable 591
 
8.0%
forces 383
 
5.2%
armed 164
 
2.2%
russian 136
 
1.8%
syrian 133
 
1.8%
idf 115
 
1.6%
defense 112
 
1.5%
coalition 109
 
1.5%
Other values (442) 2600
35.1%
2025-04-07T21:33:10.159428image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 8724
16.8%
o 4401
 
8.5%
a 3432
 
6.6%
3082
 
5.9%
e 2671
 
5.2%
i 2618
 
5.0%
k 2538
 
4.9%
U 2507
 
4.8%
w 2496
 
4.8%
l 2241
 
4.3%
Other values (55) 17136
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51846
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 8724
16.8%
o 4401
 
8.5%
a 3432
 
6.6%
3082
 
5.9%
e 2671
 
5.2%
i 2618
 
5.0%
k 2538
 
4.9%
U 2507
 
4.8%
w 2496
 
4.8%
l 2241
 
4.3%
Other values (55) 17136
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51846
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 8724
16.8%
o 4401
 
8.5%
a 3432
 
6.6%
3082
 
5.9%
e 2671
 
5.2%
i 2618
 
5.0%
k 2538
 
4.9%
U 2507
 
4.8%
w 2496
 
4.8%
l 2241
 
4.3%
Other values (55) 17136
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51846
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 8724
16.8%
o 4401
 
8.5%
a 3432
 
6.6%
3082
 
5.9%
e 2671
 
5.2%
i 2618
 
5.0%
k 2538
 
4.9%
U 2507
 
4.8%
w 2496
 
4.8%
l 2241
 
4.3%
Other values (55) 17136
33.1%
Distinct4297
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size987.5 KiB
2025-04-07T21:33:10.534108image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length1201
Median length426
Mean length175.54139
Min length27

Characters and Unicode

Total characters761323
Distinct characters88
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4281 ?
Unique (%)98.7%

Sample

1st row1 ICRC national staff killed while working in Banteay Meanchey province.
2nd row3 INGO international (Spanish) staff killed, 1 INGO international (US) staff gravely wounded during armed raid on INGO compound in Ruhengeri, Rwanda.
3rd row3 UN national staff, 1 UN international (Nigerian) staff, 1 ICRC international staff and 1 ICRC national staff kidnapped along with other (not included) UN staff, journalist and Tajik govt rep; released 48 hours later.
4th row1 INGO international staff killed by Al ittihad militia in Kismayo.
5th row1 UN national staff shot and killed in Kigali Feb 14.
ValueCountFrequency (%)
the 6287
 
4.9%
was 4189
 
3.3%
in 4152
 
3.2%
and 3468
 
2.7%
staff 3350
 
2.6%
a 3335
 
2.6%
by 2456
 
1.9%
one 2234
 
1.7%
of 2227
 
1.7%
ingo 2116
 
1.7%
Other values (7463) 94259
73.6%
2025-04-07T21:33:11.123027image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
123757
16.3%
e 72319
 
9.5%
a 59286
 
7.8%
n 48554
 
6.4%
t 46136
 
6.1%
i 42295
 
5.6%
r 39579
 
5.2%
o 37147
 
4.9%
s 30473
 
4.0%
d 28291
 
3.7%
Other values (78) 233486
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 761323
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
123757
16.3%
e 72319
 
9.5%
a 59286
 
7.8%
n 48554
 
6.4%
t 46136
 
6.1%
i 42295
 
5.6%
r 39579
 
5.2%
o 37147
 
4.9%
s 30473
 
4.0%
d 28291
 
3.7%
Other values (78) 233486
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 761323
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
123757
16.3%
e 72319
 
9.5%
a 59286
 
7.8%
n 48554
 
6.4%
t 46136
 
6.1%
i 42295
 
5.6%
r 39579
 
5.2%
o 37147
 
4.9%
s 30473
 
4.0%
d 28291
 
3.7%
Other values (78) 233486
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 761323
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
123757
16.3%
e 72319
 
9.5%
a 59286
 
7.8%
n 48554
 
6.4%
t 46136
 
6.1%
i 42295
 
5.6%
r 39579
 
5.2%
o 37147
 
4.9%
s 30473
 
4.0%
d 28291
 
3.7%
Other values (78) 233486
30.7%

Verified
Categorical

Distinct4
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size259.6 KiB
Yes
3140 
Archived
720 
Pending
475 
Pen
 
1

Length

Max length8
Median length3
Mean length4.2684502
Min length3

Characters and Unicode

Total characters18508
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowArchived
2nd rowArchived
3rd rowArchived
4th rowArchived
5th rowArchived

Common Values

ValueCountFrequency (%)
Yes 3140
72.4%
Archived 720
 
16.6%
Pending 475
 
11.0%
Pen 1
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2025-04-07T21:33:11.255868image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T21:33:11.372213image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
yes 3140
72.4%
archived 720
 
16.6%
pending 475
 
11.0%
pen 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 4336
23.4%
Y 3140
17.0%
s 3140
17.0%
i 1195
 
6.5%
d 1195
 
6.5%
n 951
 
5.1%
A 720
 
3.9%
r 720
 
3.9%
c 720
 
3.9%
h 720
 
3.9%
Other values (3) 1671
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18508
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4336
23.4%
Y 3140
17.0%
s 3140
17.0%
i 1195
 
6.5%
d 1195
 
6.5%
n 951
 
5.1%
A 720
 
3.9%
r 720
 
3.9%
c 720
 
3.9%
h 720
 
3.9%
Other values (3) 1671
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18508
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4336
23.4%
Y 3140
17.0%
s 3140
17.0%
i 1195
 
6.5%
d 1195
 
6.5%
n 951
 
5.1%
A 720
 
3.9%
r 720
 
3.9%
c 720
 
3.9%
h 720
 
3.9%
Other values (3) 1671
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18508
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4336
23.4%
Y 3140
17.0%
s 3140
17.0%
i 1195
 
6.5%
d 1195
 
6.5%
n 951
 
5.1%
A 720
 
3.9%
r 720
 
3.9%
c 720
 
3.9%
h 720
 
3.9%
Other values (3) 1671
 
9.0%

Source
Categorical

Distinct11
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size287.6 KiB
Official Report
1597 
Focal Point
1178 
Archived
704 
Media
622 
ACLED
199 
Other values (6)
 
37

Length

Max length16
Median length15
Mean length10.881946
Min length5

Characters and Unicode

Total characters47195
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowArchived
2nd rowArchived
3rd rowArchived
4th rowArchived
5th rowArchived

Common Values

ValueCountFrequency (%)
Official Report 1597
36.8%
Focal Point 1178
27.2%
Archived 704
16.2%
Media 622
 
14.3%
ACLED 199
 
4.6%
Official Report 29
 
0.7%
Focal point 3
 
0.1%
ACLED 2
 
< 0.1%
media 1
 
< 0.1%
Offiicial Report 1
 
< 0.1%

Length

2025-04-07T21:33:11.511412image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
report 1628
22.8%
official 1627
22.8%
focal 1181
16.5%
point 1181
16.5%
archived 704
9.9%
media 623
 
8.7%
acled 201
 
2.8%
offiicial 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 5765
12.2%
o 3990
 
8.5%
c 3513
 
7.4%
a 3432
 
7.3%
f 3256
 
6.9%
e 2955
 
6.3%
2840
 
6.0%
l 2809
 
6.0%
t 2809
 
6.0%
r 2333
 
4.9%
Other values (16) 13493
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47195
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 5765
12.2%
o 3990
 
8.5%
c 3513
 
7.4%
a 3432
 
7.3%
f 3256
 
6.9%
e 2955
 
6.3%
2840
 
6.0%
l 2809
 
6.0%
t 2809
 
6.0%
r 2333
 
4.9%
Other values (16) 13493
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47195
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 5765
12.2%
o 3990
 
8.5%
c 3513
 
7.4%
a 3432
 
7.3%
f 3256
 
6.9%
e 2955
 
6.3%
2840
 
6.0%
l 2809
 
6.0%
t 2809
 
6.0%
r 2333
 
4.9%
Other values (16) 13493
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47195
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 5765
12.2%
o 3990
 
8.5%
c 3513
 
7.4%
a 3432
 
7.3%
f 3256
 
6.9%
e 2955
 
6.3%
2840
 
6.0%
l 2809
 
6.0%
t 2809
 
6.0%
r 2333
 
4.9%
Other values (16) 13493
28.6%

Interactions

2025-04-07T21:32:53.146391image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:36.188495image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:39.174199image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:42.440202image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:45.783882image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:48.645041image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:51.450815image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:54.294085image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:57.406420image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:00.368457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:03.389794image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:06.292345image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:09.277438image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:12.168674image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:15.250571image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:18.129602image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:21.074304image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:24.013440image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:27.100473image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:30.196474image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:33.106983image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:35.959336image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:38.953977image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:41.768505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:44.833823image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:47.774572image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:50.416797image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:53.246390image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:36.305127image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:39.270004image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:42.557273image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:45.880281image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:48.848307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:51.530710image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:54.384884image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:57.503702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:00.459212image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:03.488767image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:06.394234image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:09.377448image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:12.268855image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:15.348408image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:18.228949image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:21.172295image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:24.113190image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:27.198216image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:30.295579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:33.207526image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:36.192795image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:39.036321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:41.868334image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:44.933631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:47.850758image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:50.498166image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:53.347433image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:36.427376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:39.413105image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:42.674444image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:45.972144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:48.949859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:51.635480image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:54.495552image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:57.606202image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:00.695492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:03.588063image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:06.499082image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:09.479058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:12.368877image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:15.434648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-04-07T21:32:36.296848image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:39.136697image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:41.976444image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:45.033500image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:47.950733image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:50.596428image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:53.445889image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:36.537830image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:39.524226image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:42.784980image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:46.086835image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:49.050135image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:51.730043image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:54.605596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:57.711956image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:00.797680image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:03.687171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:06.600264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:09.579512image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:12.606990image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:15.547555image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:18.429662image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:21.382860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:24.329899image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:27.417501image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:30.498384image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:33.404832image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:36.398704image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:39.236696image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:42.076018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:45.133553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:48.055720image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:50.680736image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:53.545249image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:36.648863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:39.637484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:42.886075image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:46.201719image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:49.145772image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:51.833262image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:54.759580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:31:57.818794image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:00.908481image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:03.774206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:06.700439image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:09.683225image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:12.712960image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-04-07T21:32:33.006759image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:35.852529image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:38.837594image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:41.668339image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:44.733919image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:47.674103image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:50.319867image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-04-07T21:32:53.062344image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2025-04-07T21:32:56.117692image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-07T21:32:56.644991image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-07T21:32:56.995432image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Incident IDYearMonthDayCountry CodeCountryRegionDistrictCityUNINGOICRCNRCS and IFRCNNGOOtherNationals killedNationals woundedNationals kidnappedTotal nationalsInternationals killedInternationals woundedInternationals kidnappedTotal internationalsTotal killedTotal woundedTotal kidnappedTotal affectedGender MaleGender FemaleGender UnknownMeans of attackAttack contextLocationLatitudeLongitudeMotiveActor typeActor nameDetailsVerifiedSource
0119971.0NaNKHCambodiaBanteay MeancheyNaNNaN001.00.00.00100100001001001UnknownUnknownUnknown14.070929103.099916UnknownUnknownUnknown1 ICRC national staff killed while working in Banteay Meanchey province.ArchivedArchived
1219971.0NaNRWRwandaNorthernMusanzeRuhengeri040.00.00.00000031043104310ShootingRaidOffice/compound-1.49984029.634970UnknownUnknownUnknown3 INGO international (Spanish) staff killed, 1 INGO international (US) staff gravely wounded during armed raid on INGO compound in Ruhengeri, Rwanda.ArchivedArchived
2319972.0NaNTJTajikistanNaNNaNNaN402.00.00.00004400220066006KidnappingUnknownUnknown38.62817370.815654NaNUnknownUnknown3 UN national staff, 1 UN international (Nigerian) staff, 1 ICRC international staff and 1 ICRC national staff kidnapped along with other (not included) UN staff, journalist and Tajik govt rep; released 48 hours later.ArchivedArchived
3419972.0NaNSOSomaliaLower JubaKismayoKismayo010.00.00.00000010011001001UnknownUnknownUnknown-0.35821642.545087PoliticalNon-state armed group: RegionalAl-Itihaad al-Islamiya1 INGO international staff killed by Al ittihad militia in Kismayo.ArchivedArchived
4519972.014.0RWRwandaKigaliKigaliKigali100.00.00.00100100001001001ShootingIndividual attackUnknown-1.95085130.061508PoliticalUnknownUnknown1 UN national staff shot and killed in Kigali Feb 14.ArchivedArchived
5719975.0NaNCDDR CongoNaNNaNNaN000.010.00.0010001000001000100010UnknownCombat/CrossfireUnknown-2.98143423.822264IncidentalNon-state armed group: NationalAlliance of Democratic Forces for the Liberation of Congo-Zaire (ADFL)10 NRCS staff first aid workers killed in fighting between Zairean troops and rebels from the Alliance of Democratic Forces for the Liberation of Congo-Zaire (ADFL).ArchivedArchived
6619975.07.0SLSierra LeoneNaNNaNNaN300.00.00.00120300001203102UnknownAmbushRoad8.640035-11.840027UnknownUnknownUnknown1 UN national staff driver killed and 2 UN national staff wounded when UN assessment team ambushed May 7.ArchivedArchived
71119976.0NaNSOSomaliaGedoBaardheereTulo-Burago100.00.00.00100100001001100Kidnap-killingAmbushRoad2.25000041.166670IncidentalNon-state armed group: RegionalAl-Itihaad al-Islamiya1 UN national staff beheaded by Al-Ittihad militia after ambush near Tulo-Burago village in Gedo region. Another 3 UN national staff (not included) bound, questioned and later released. The victim, a senior SNF Commander, was the apparent target of the Al-Ittihad militia.YesFocal Point
81219976.08.0RWRwandaNorthernMusanzeRuhengeri010.00.00.00100100001001100UnknownCombat/CrossfireUnknown-1.49984029.634970IncidentalUnknownUnknown1 INGO national staff killed when he was among a group of villagers attacked by insurgents near Ruhengeri on June 8.ArchivedArchived
9819976.014.0RWRwandaNorthernMusanzeRuhengeri100.00.00.00100100001001100ShootingIndividual attackHome-1.49984029.634970PoliticalNon-state armed group: UnknownNot applicable1 UN national staff shot and killed in Ruhengeri June 14. The staffer, his wife, young child and another relative were killed at night in their home in Rubange secteur.ArchivedArchived
Incident IDYearMonthDayCountry CodeCountryRegionDistrictCityUNINGOICRCNRCS and IFRCNNGOOtherNationals killedNationals woundedNationals kidnappedTotal nationalsInternationals killedInternationals woundedInternationals kidnappedTotal internationalsTotal killedTotal woundedTotal kidnappedTotal affectedGender MaleGender FemaleGender UnknownMeans of attackAttack contextLocationLatitudeLongitudeMotiveActor typeActor nameDetailsVerifiedSource
4327448420252.08.0SDSudanKhartoumKhartoumKhartoum000.00.02.00002200000022002KidnappingCombat/CrossfireProject site15.48906632.552141UnknownNon-state armed group: NationalRapid Support Forces (RSF)Two NNGO aid workers were kidnapped by the RSF at the Bashair Teaching Hospital in southern Khartoum. Their whereabouts remain unknown.PendingMedia
4328447520252.011.0SSSouth SudanJongleiUrorMwo tot000.00.02.00200200002002200ShootingAmbushProject site8.20370932.037602EconomicCriminalNot applicableTwo security guards of an INGO implementing partner were shot and killed when armed criminals robbed the INGO distribution point in Mowt Tot, Uror, Jonglei.PendingOfficial Report
4329450620252.011.0AFAfghanistanKandaharKandaharKandahar010.00.00.00010100000101001Bodily assaultRaidOffice/compound31.61857065.715588PoliticalHost stateTalibanAt least one INGO aid worker was beaten and injured when the Taliban forces entered the INGO office and expelled all the workers due to the removal of their license to operate in the country in Kandahar.PendingACLED
4330450720252.013.0PKPakistanKhyber PakhtunkhwaSouth WaziristanAzam Warsak000.01.00.00001100000011001KidnappingUnknownProject site32.28913669.428782EconomicNon-state armed group: NationalTehrik-i-Taliban Pakistan (TTP)One NRCS health worker was abducted by allegedly Tehrik-i-Taliban Pakistan (TTP) members at a health facility in Azam Warsak, South Waziristan. The reason for the abduction was economic.PendingACLED
4331447620252.014.0SDSudanNorth DarfurAl FasherZamzam Refugee Camp000.00.02.00200200002002002UnknownUnknownProject site13.48833425.309852UnknownUnknownUnknownTwo aid workers were killed by unknown armed men in the ZamZam refugee camp, Al Fasher, North Darfur.PendingOfficial Report
4332447920252.017.0SDSudanNorth DarfurAl FasherAl Fasher100.00.00.00100100001001100ShellingCombat/CrossfireHome13.59399825.361448IncidentalUnknownUnknownOne UN-contracted aid worker was killed during a shelling that hit his home in Al Fasher, North Darfur.PendingOfficial Report
4333448020252.020.0CDDR CongoNorth KivuMasisiMasisi010.00.00.00100100001001100ShootingCombat/CrossfireOffice/compound-1.39944128.819487IncidentalNon-state armed group: SubnationalMarch 23 Movement (M23)One INGO aid worker was shot when bullets from a confrontation between M-23 and VDP/Wazalendo hit the INGO warehouse. The aid worker later died due to the injuries.PendingOfficial Report
4334450820252.026.0NGNigeriaBornoMongunoMonguno020.00.00.00002200000022002KidnappingRaidProject site12.66883913.611683UnknownNon-state armed group: RegionalIslamic State of West Africa ProvinceTwo INGO aid workers were abducted by the ISWAP during a raid in Monguno, Borno. Their whereabouts remain unknown.PendingACLED
4335450220253.06.0SYSyrian Arab RepublicLatakiaLatakiaJableh100.00.00.00100100001001001ShootingCombat/CrossfireRoad35.36189235.930271IncidentalUnknownUnknownOne UN aid worker was shot and killed when caught in a crossfire in Jableh, driving to Latakia, Latakia governorate.PendingOfficial Report
43364386202512.019.0NGNigeriaBornoBornoZari000.00.02.00200200002002002ShootingAmbushRoad13.07201812.729489PoliticalNon-state armed group: RegionalIslamic State of West Africa ProvinceTwo NNGO aid workers were shot and killed during an ambush by ISWAP militants in Zari, Borno state.PendingMedia